Happy birthday easystats! A retrospective
Jan 27, 2022
Happy birthday easystats!
Two years ago, which feels like yesterday, we celebrated the easystats project’s first anniversary.
Wow, those were simpler times! One could travel for pleasure, party with dozens of people and have a face-to-face conversation, and the thing that spread like a wildfire around the globe was everyone’s obsession with The Weeknd’s Blinding Lights!
At any rate, since the pandemic did not affect GitHub in any way, our free and open-source fanatic souls have made sure that this project keeps progressing, sometimes in leaps and bounds, while other times slow as grey clouds in the sky.
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You write R packages and functions? This package will change your life!
Feb 1, 2021
What is it?
We are talking about the insight package. It is what allows other packages, like easystats (parameters, effectsize, performance, report, …) or ggstatsplot, sjstats or modelsummary to be as powerful as they are, supporting tons of different R models. So why make you life hard when you can be like them, and rely on insight?
It is made for developers (and users) that do some postprocessing of different models (e.
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New formatting features in the parameters package
Jan 21, 2021
You probably already have heard of the parameters package, a light-weight package to extract, compute and explore the parameters of statistical models using R (if not, there is a related publication introducing the package’s main features).
In this post, we like to introduce a new feature that facilitates nicely rendered output in markdown or HTML format (including PDFs). This allows you to easily create pretty tables of model summaries, for a large variety of models.
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In defence of the 95% CI
May 12, 2020
TLDR: BayestestR currently uses a 89% threshold by default for Credible Intervals (CI). Should we change that? If so, by what? Join the discussion here.
Magical numbers, or conventional thresholds, have bad press in statistics, and there are many of them. For instance, .05 (for the p-value), or the 95% range for the Confidence Interval (CI). Indeed, why 95 and not 94 or 90?
One of the issue that traditional confidence intervals are often being interpreted as a description of the uncertainty surrounding a parameter’s value.
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Multilevel Correlations: A New Method for Common Problems
Apr 14, 2020
In this tutorial, we will introduce multilevel correlations (or hierarchical / random-effects correlations) and how to compute them using the new correlations package from the easystats suite.
You can install the updated version and load the package as follows:
install.packages("correlation")
library(correlation)
Data
Imagine we have an experiment in which 10 individuals completed a task with 100 trials. For each of the 1000 total trials, we measured two things, V1 and V2, and our research aims at investingating the link between these two variables.
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The ulimate package for correlations (by easystats)
Mar 18, 2020
The correlation package
The easystats project continues to grow with its more recent addition, a package devoted to correlations. Check-out its webpage here!
It’s lightweight, easy to use, and allows for the computation of many different kinds of correlations, such as partial correlations, Bayesian correlations, multilevel correlations, polychoric correlations, biweight, percentage bend or Sheperd’s Pi correlations (types of robust correlation), distance correlation (a type of non-linear correlation) and more, also allowing for combinations between them (for instance, Bayesian partial multilevel correlation).
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The p-direction: A Bayesian equivalent of the p-value?
Feb 26, 2020
The Bayesian framework is powerful and allows for an incredible amount of flexibility and control over your analysis. That being said, newcomers often struggle with a lot of new concepts and tools and could benefit from some familiar grounding. And the p-value is a very familiar index (although paradoxically often misunderstood, but that’s another topic).
Is there an equivalent of the p-value? Well, depends on what “equivalent” means.
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easystats: one year already. What's next?
Jan 23, 2020
Happy birthday easystats!
About a year ago, I (Dom) sadly realised that the R package I was maintaining (psycho) was drifting more and more away from its original scope, getting drown under a pile of unrelated and under-documented features that I kept on adding as my R skills improved. Something had to be done.
I decided to get in touch with Daniel, aka strengejacke (for mysterious and very confusing reasons), the author of the sjverse, a collection of awesome packages which scope wasn’t too distant from my own one.
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Comparison of indices of significance in the Bayesian framework
Dec 17, 2019
The bayestestR package has several functions to compute indices of effect existence and significance in a Bayesian framework, like p_direction() or bayesfactor_parameters().
The accuracy of these indices is affected by various sources of uncertainty, such as sample size or noise. Using the package, we have created a small animation that demontrates how new evidence updates the posterior distribution and thereby indices of existence and significance:
If you’d like to know more (statistical) details about these indices, we have recently published a paper with a simulation study (available for free!
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News from easystats: updated parameters and see packages.
Nov 25, 2019
New Features of the parameters and see Package
We’re excited to announce some news from the easystats-project. Two packages were updated recently, the parameters-package and our visualization-toolbox, the see-package.
Before we start introducing some of the new features, we’d like to explain why you need the see-package to create plots for functions from other easystats packages. So, the see-package not only includes additional geoms, color scales and themes for ggplot2, but - maybe more important - also provides plot()-methods for many functions from the various easystats packages.
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